arXiv subjects

Florian Atzenhofer-Baumgartner

Publications and source records attributed to Florian Atzenhofer-Baumgartner.

6 recordsLinked to original sources

arXiv2026

Design and Operation of a Federated GPU Cluster for Digital Humanities within DHinfra.at

We describe the design, implementation, and operation of a small federated GPU cluster built for Digital Humanities (DH) research within the Austrian DHinfra.at project. The system spans two university sites, brokers logins from a national identity federation, and exposes compute through three interfaces: interactive notebooks, SSH, and an OpenAI-compatible inference API. The core of the platform is a control plane that maps federated identity, per-project quotas, and a model catalogue onto self-service interfaces. We document this control plane in detail, including the request-and-approval queue that mediates every privileged cluster effect and the two-tier model-serving setup, which combines always-on services with on-demand model swapping and lets researchers consume large language models over HTTPS without holding a cluster account. We report the constraints that shaped the build: a fixed power budget, EU-wide procurement, a team of about 1.5 full-time equivalents, and the relationship between prioritized access and utilization. We also record the main build decisions against the alternatives we researched and did not adopt. The platform is released under the Apache License 2.0. We close with a playbook for teams building a domain-specific GPU cluster on a limited budget. This is a living technical report, versioned and updated as the platform evolves.

cs.DC↗

arXiv2026

What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives

User models for recommender systems (RecSys) typically assume stable preferences, similarity-based relevance, and session-bounded interactions -- assumptions derived from high-volume consumer contexts. This paper investigates these assumptions for humanities scholars working with digital archives. Following a human-centered design approach, we conducted focus groups and analyzed interview data from 18 researchers. Our analysis identifies four dimensions where scholarly information-seeking diverges from common RecSys user modeling: (1) context volatility -- preferences shift with research tasks and domain expertise; (2) epistemic trust -- relevance depends on verifiable provenance; (3) contrastive seeking -- researchers seek items that challenge their current direction; and (4) strand continuity -- research spans long-term threads rather than discrete sessions. We discuss implications for user modeling and outline how these dimensions relate to collaborative filtering, content-based, and session-based recommendation. We propose these dimensions as a diagnostic framework applicable beyond archives to similar application domains where typical user modeling assumptions may not hold.

cs.DL↗

arXiv2025

Is text normalization relevant for classifying medieval charters?

This study examines the impact of historical text normalization on the classification of medieval charters, specifically focusing on document dating and locating. Using a data set of Middle High German charters from a digital archive, we evaluate various classifiers, including traditional and transformer-based models, with and without normalization. Our results indicate that the given normalization minimally improves locating tasks but reduces accuracy for dating, implying that original texts contain crucial features that normalization may obscure. We find that support vector machines and gradient boosting outperform other models, questioning the efficiency of transformers for this use case. Results suggest a selective approach to historical text normalization, emphasizing the significance of preserving some textual characteristics that are critical for classification tasks in document analysis.

cs.CL↗

arXiv2025

A Multistakeholder Approach to Value-Driven Co-Design of Recommender System Evaluation Metrics in Digital Archives

This paper presents the first multistakeholder approach for translating diverse stakeholder values into an evaluation metric setup for Recommender Systems (RecSys) in digital archives. While commercial platforms mainly rely on engagement metrics, cultural heritage domains require frameworks that balance competing priorities among archivists, platform owners, researchers, and other stakeholders. To address this challenge, we conducted high-profile focus groups (5 groups x 5 persons) with upstream, provider, system, consumer, and downstream stakeholders, identifying value priorities across critical dimensions: visibility/representation, expertise adaptation, and transparency/trust. Our analysis shows that stakeholder concerns naturally align with four sequential research funnel stages: discovery, interaction, integration, and impact. The resulting evaluation setup addresses domain-specific challenges including collection representation imbalances, non-linear research patterns, and tensions between specialized expertise and broader accessibility. We propose directions for tailored metrics in each stage of this research journey, such as research path quality for discovery, contextual appropriateness for interaction, metadata-weighted relevance for integration, and cross-stakeholder value alignment for impact assessment. Our contributions extend beyond digital archives to the broader RecSys community, offering transferable evaluation approaches for domains where value emerges through sustained engagement rather than immediate consumption.

cs.IR↗

arXiv2024

Challenges in Implementing a Recommender System for Historical Research in the Humanities

This extended abstract describes the challenges in implementing recommender systems for digital archives in the humanities, focusing on Monasterium.net, a platform for historical legal documents. We discuss three key aspects: (i) the unique characteristics of so-called charters as items for recommendation, (ii) the complex multi-stakeholder environment, and (iii) the distinct information-seeking behavior of scholars in the humanities. By examining these factors, we aim to contribute to the development of more effective and tailored recommender systems for (digital) humanities research.

cs.IR↗

arXiv2024

Value Identification in Multistakeholder Recommender Systems for Humanities and Historical Research: The Case of the Digital Archive Monasterium.net

Recommender systems remain underutilized in humanities and historical research, despite their potential to enhance the discovery of cultural records. This paper offers an initial value identification of the multiple stakeholders that might be impacted by recommendations in Monasterium.net, a digital archive for historical legal documents. Specifically, we discuss the diverse values and objectives of its stakeholders, such as editors, aggregators, platform owners, researchers, publishers, and funding agencies. These in-depth insights into the potentially conflicting values of stakeholder groups allow designing and adapting recommender systems to enhance their usefulness for humanities and historical research. Additionally, our findings will support deeper engagement with additional stakeholders to refine value models and evaluation metrics for recommender systems in the given domains. Our conclusions are embedded in and applicable to other digital archives and a broader cultural heritage context.

cs.IR↗